Establishment of a nomogram that predicts the risk of heart failure in hemodialysis patients

Area of Science:

• Nephrology
• Cardiology
• Medical Informatics

Background:

• Chronic kidney disease (CKD) is a growing global health concern, projected to be the fifth leading cause of death by 2040.
• Cardiovascular disease (CVD), especially heart failure (HF), is a major complication for patients undergoing hemodialysis.
• Effective risk stratification is crucial for managing HF in this vulnerable population.

Purpose of the Study:

• To develop and validate a predictive nomogram for heart failure hospitalization risk in hemodialysis patients.
• To provide a clinical decision-making tool for early identification of high-risk individuals.
• To potentially improve patient outcomes and reduce healthcare burden associated with HF hospitalizations.

Main Methods:

• Retrospective analysis of data from 196 hemodialysis patients.
• Identification of significant predictors using univariate and multivariate logistic regression.
• Development of a nomogram incorporating age, diabetes, dialysis duration, left ventricular mass index (LVMI), albumin (ALB), and ejection fraction (EF).

Main Results:

• The developed nomogram demonstrated good predictive discrimination (AUC = 0.728).
• The model showed excellent calibration, with no significant lack of fit (Hosmer-Lemeshow test, P = 0.463).
• Decision curve analysis confirmed the nomogram's clinical utility and applicability across various risk thresholds.

Conclusions:

• The validated nomogram serves as a valuable tool for predicting heart failure hospitalization risk in hemodialysis patients.
• Clinicians can utilize this nomogram to identify at-risk individuals, enabling targeted interventions.
• Implementation of this tool may lead to improved management strategies and a reduction in HF-related hospitalizations within the hemodialysis population.